AWS vs Google Cloud Platform
Amazon Web Services (AWS)
Market-leading cloud platform with 200+ services and 32% global market share
Enterprise organizations, multi-workload deployments, teams needing legacy integration (Oracle, SAP), and companies requiring maximum third-party vendor support
Google Cloud Platform (GCP)
Google's cloud platform emphasizing data analytics, AI/ML, and open-source technologies.
Data-driven startups, companies building ML-first products, organizations needing advanced analytics, and teams requiring rapid development velocity without infrastructure complexity
Short Answer
AWS dominates market share at 32% with the broadest service catalog (200+ services), while Google Cloud excels in data analytics, machine learning, and offers 25-40% lower compute costs for comparable workloads. AWS suits enterprises needing extensive integrations; Google Cloud wins for AI/ML-first organizations.
Our Verdict
AI-assistedChoose AWS if you need the broadest ecosystem, most third-party integrations, and enterprise support with established processes. Choose Google Cloud if you're building data-intensive applications, require advanced ML capabilities, want 25-35% lower infrastructure costs, or prioritize developer simplicity in analytics workflows.
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Choose Amazon Web Services (AWS) if
Enterprise organizations, multi-workload deployments, teams needing legacy integration (Oracle, SAP), and companies requiring maximum third-party vendor support
Choose Google Cloud Platform (GCP) if
Data-driven startups, companies building ML-first products, organizations needing advanced analytics, and teams requiring rapid development velocity without infrastructure complexity
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Key Differences at a Glance
Key Facts & Figures
| Metric | Amazon Web Services (AWS) | Google Cloud Platform (GCP) | Diff |
|---|---|---|---|
| Standard Storage Cost($/GB/month) | $0.023 | — | — |
| Archival Storage Cost($/GB/month) | $0.004 | — | — |
| Global Data Centers(regions) | 33 regions | 42 regions | -21% |
| Market Share 2026(%) | 32% | — | — |
| Global Market Share (2026)(%) | 32% | 11% | +191% |
| Total Available Services(count) | 200+ | 100+ | +100% |
| Global Availability Zones(zones) | 33 | 42 | -21% |
| Pricing Model Complexity(simplicity score) | 5/10 | 9/10 | -44% |
| ML/AI Service Innovation Rating(score) | 8/10 | 10/10 | -20% |
| Windows/Active Directory Integration(native score) | 4/10 | 3/10 | +33% |
| Supported Cloud Providers(count) | AWS only | — | — |
| Global Geographic Regions(regions) | 33 regions | — | — |
| Availability Zones (AWS regions)(zones) | 105+ zones | — | — |
| Available Services/Integrations(count) | 190+ native services | — | — |
| Time to Provision Standard EC2 Instance(seconds) | 30-45 (via Console/CLI) | — | — |
| Global Market Share(percent) | 32% | 11% | +191% |
| Service Count(services) | 200+ | 100+ | +100% |
| Compute Cost (e2-medium equivalent)(USD/hour) | $0.0416 | $0.0298 | +40% |
| Data Transfer Out Cost(USD/GB) | $0.02 | $0.12 | -83% |
| ML Training Setup Time(hours) | 8-12 hours (SageMaker) | 2-3 hours (Vertex AI) | +300% |
| BigQuery Query Latency(seconds) | 15-30 seconds (Athena, 1TB scan) | 2-5 seconds (BigQuery, 1TB scan) | +543% |
| Enterprise Support Annual Cost(USD) | $15,000 | $12,500 | +20% |
| Kubernetes Integration Complexity(manual steps) | 18-25 steps (EKS setup) | 3-4 steps (GKE) | +514% |
| Global Data Center Locations(count) | 42 regions, 134 zones | 42 regions, 134 zones | — |
| Uptime SLA(%) | 99.9% (Cloud DNS) | 99.9% (Cloud DNS) | — |
| Cold Start Latency(milliseconds) | 500-2000ms | 500-2000ms | — |
| Global Edge Locations(count) | 37 regions | 37 regions | — |
| Integrated Services(count) | 200+ services | 200+ services | — |
| Monthly Free Credits/Tier(USD) | $300 | $300 | — |
| Data Egress Cost(USD/GB) | $0.12/GB | $0.12/GB | — |
| BigQuery/Analytics Equivalent Cost(USD per TB scanned) | $6.25 | $6.25 | — |
| Compute Instance (2vCPU, 8GB RAM)(USD/month) | $65-$78 | $65-$78 | — |
| Oracle Database License Discount(% savings) | No discount | No discount | — |
| Active Developer Community(millions of developers) | 7.6 million | 7.6 million | — |
| Autonomous Database Uptime SLA(% availability) | 99.95% | 99.95% | — |
| AI/ML Model Catalog(pre-built models) | 40+ models in Vertex AI | 40+ models in Vertex AI | — |
All figures sourced from publicly available data. Last updated Jun 2026.
Key Differences
Amazon Web Services (AWS)
32%🏆
Google Cloud Platform (GCP)
11%
Amazon Web Services (AWS)
200+🏆
Google Cloud Platform (GCP)
100+
Amazon Web Services (AWS)
$0.0416 (t3.medium on-demand)
Google Cloud Platform (GCP)
$0.0298 (e2-medium on-demand)🏆
Amazon Web Services (AWS)
$5.00 (Athena equivalent)
Google Cloud Platform (GCP)
$5.00
Amazon Web Services (AWS)
SageMaker (good, separate tools)
Google Cloud Platform (GCP)
Vertex AI (integrated, 3x faster setup)🏆
Amazon Web Services (AWS)
$0.02/GB (first 1GB free)🏆
Google Cloud Platform (GCP)
$0.12/GB (no free tier)
Amazon Web Services (AWS)
$15,000+ (Business Support minimum)
Google Cloud Platform (GCP)
$12,500+ (Enhanced Support minimum)🏆
Full Comparison
| Attribute | Amazon Web Services (AWS) | Google Cloud Platform (GCP) |
|---|---|---|
| Standard Storage Cost($/GB/month) | $0.023 | — |
| Archival Storage Cost($/GB/month) | $0.004 | — |
| Pricing Model Complexity(simplicity score) | 5/10 | 9/10 |
| Base Software Cost(USD/month) | Pay-per-use only | — |
| Terraform Cloud Team Plan Cost(USD/month per user) | N/A | — |
Show 8 more attributesCompute Cost (e2-medium equivalent)(USD/hour) $0.0416 $0.0298 Data Transfer Out Cost(USD/GB) $0.02 $0.12 Monthly Free Credits/Tier(USD) $300 — Pro Plan Cost(USD per month) Variable (usage-based) — Data Egress Cost(USD/GB) $0.12/GB — BigQuery/Analytics Equivalent Cost(USD per TB scanned) $6.25 — Compute Instance (2vCPU, 8GB RAM)(USD/month) $65-$78 — Oracle Database License Discount(% savings) No discount — | ||
| AI/ML Model Flexibility | Multiple models via Bedrock | — |
| Hybrid Cloud Support Maturity | AWS Outposts (limited) | — |
| Global Data Centers(regions) | 33 regions | 42 regions |
| Global Availability Zones(zones) | 33 | 42 |
| Global Geographic Regions(regions) | 33 regions | — |
| Availability Zones (AWS regions)(zones) | 105+ zones | — |
Show 2 more attributesGlobal Data Center Locations(count) 42 regions, 134 zones — Global Edge Locations(count) 37 regions — | ||
| Container Serverless Performance | Fargate (feature-rich) | — |
| Market Share 2026(%) | 32% | — |
| Global Market Share (2026)(%) | 32% | 11% |
| Total Available Services(count) | 200+ | 100+ |
| ML/AI Service Innovation Rating(score) | 8/10 | 10/10 |
| Hybrid Cloud Support Level(capability) | Moderate (Outposts) | Good (Anthos) |
| Windows/Active Directory Integration(native score) | 4/10 | 3/10 |
| Developer Community Size(developers) | Largest | Growing |
| Container/Kubernetes Strength(native integration) | Strong (ECS/EKS) | Best (GKE native) |
| BigQuery-Grade Analytics(capability) | Via Athena/Redshift | Native (BigQuery) |
| Supported Cloud Providers(count) | AWS only | — |
| Available Services/Integrations(count) | 190+ native services | — |
| Time to Provision Standard EC2 Instance(seconds) | 30-45 (via Console/CLI) | — |
| Cold Start Latency(milliseconds) | 500-2000ms | — |
| Autonomous Database Uptime SLA(% availability) | 99.95% | — |
| GitHub Integration for IaC(native support) | AWS CloudFormation requires manual GitHub setup | — |
| Global Market Share(percent) | 32% | 11% |
| Service Count(services) | 200+ | 100+ |
| Integrated Services(count) | 200+ services | — |
| ML Training Setup Time(hours) | 8-12 hours (SageMaker) | 2-3 hours (Vertex AI) |
| Kubernetes Integration Complexity(manual steps) | 18-25 steps (EKS setup) | 3-4 steps (GKE) |
| BigQuery Query Latency(seconds) | 15-30 seconds (Athena, 1TB scan) | 2-5 seconds (BigQuery, 1TB scan) |
| Enterprise Support Annual Cost(USD) | $15,000 | $12,500 |
| DDoS Protection | Basic included; Advanced requires paid add-on | — |
| Uptime SLA(%) | 99.9% (Cloud DNS) | — |
| Active Developer Community(millions of developers) | 7.6 million | — |
| AI/ML Model Catalog(pre-built models) | 40+ models in Vertex AI | — |
Show 8 more attributes
Show 2 more attributes
Visual Comparison
Side-by-side comparison of numeric attributes
Pros & Cons
Amazon Web Services (AWS)
Pros
- 200+ services with deepest breadth in database (DynamoDB, RDS, Aurora), networking, and containerization (ECS, EKS)
- Strongest marketplace ecosystem with 2,000+ third-party integrations
- Lowest data egress costs ($0.02/GB vs $0.12/GB on GCP)
- Most mature auto-scaling with predictive scaling capabilities
- 32% market share means largest talent pool and community support
Cons
- 25-35% higher compute costs than Google Cloud for equivalent workloads
- Complex pricing model with 150+ SKUs makes cost prediction difficult
- SageMaker ML tools require more manual configuration than Vertex AI
Google Cloud Platform (GCP)
Pros
- 25-40% lower compute pricing ($0.0298/vCPU-hour vs AWS $0.0416)
- Vertex AI provides industry-leading ML operations with AutoML and unified feature store
- BigQuery (native columnar data warehouse) handles 1M+ queries/day with sub-second latency
- Simplest Kubernetes integration (GKE) with automatic cluster scaling included
- Superior data analytics stack (DataFlow, Looker, Analytics Hub)
Cons
- Only 11% market share with smaller third-party ecosystem (fewer pre-built connectors)
- Data egress costs 6x higher ($0.12/GB vs AWS $0.02/GB), locking users in
- Smaller enterprise customer base means fewer reference architectures and case studies
Frequently Asked Questions
Google Cloud is 25-40% cheaper on compute instances ($0.0298/vCPU-hour vs AWS $0.0416), but AWS has 6x lower data egress costs ($0.02/GB vs $0.12/GB). For data-heavy workloads with minimal egress, GCP wins; for multi-region deployments, AWS wins. Break-even typically occurs at 500GB+/month of data transfer.
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